连接过去与未来：结合历史预测与规划的端到端自动驾驶
Bridging Past and Future: End-to-End Autonomous Driving with Historical Prediction and Planning
AI总结：
针对现有端到端自动驾驶历史信息利用范式的缺陷，提出BridgeAD框架，通过多步查询设计按时间步整合历史预测与规划信息，提升感知与规划效果，在nuScenes数据集上达到SOTA性能。
摘要：
端到端自动驾驶在可微框架中统一各任务，支持面向规划的优化，正受到越来越多的关注。现有方法要么通过密集的历史鸟瞰图（BEV）特征，要么通过查询稀疏记忆库来聚合历史信息，沿用了从检测任务继承来的范式。但我们认为，这些范式要么在运动规划中遗漏了历史信息，要么无法匹配运动规划的多步属性——该属性需要预测或规划多个未来时间步。秉承“未来是过去的延续”的理念，我们提出BridgeAD，将运动与规划查询重构为多步查询，以区分每个未来时间步的查询。这种设计能将历史预测与规划结果按时间步应用到端到端系统的对应部分，从而有效利用这些信息，同时提升感知与运动规划效果。具体而言，当前帧的历史查询与感知模块结合，未来帧的查询则与运动规划模块整合。通过这种方式，我们在每个时间步聚合历史洞察，弥合了过去与未来之间的差距，增强了端到端自动驾驶流水线的整体一致性与准确性。在nuScenes数据集上开展的开环与闭环设置下的大量实验表明，BridgeAD取得了当前最优（SOTA）性能。
英文摘要：
End-to-end autonomous driving unifies tasks in a differentiable framework, enabling planning-oriented optimization and attracting growing attention. Current methods aggregate historical information either through dense historical bird's-eye-view (BEV) features or by querying a sparse memory bank, following paradigms inherited from detection. However, we argue that these paradigms either omit historical information in motion planning or fail to align with its multi-step nature, which requires predicting or planning multiple future time steps. In line with the philosophy of future is a continuation of past, we propose BridgeAD, which reformulates motion and planning queries as multi-step queries to differentiate the queries for each future time step. This design enables the effective use of historical prediction and planning by applying them to the appropriate parts of the end-to-end system based on the time steps, which improves both perception and motion planning. Specifically, historical queries for the current frame are combined with perception, while queries for future frames are integrated with motion planning. In this way, we bridge the gap between past and future by aggregating historical insights at every time step, enhancing the overall coherence and accuracy of the end-to-end autonomous driving pipeline. Extensive experiments on the nuScenes dataset in both open-loop and closed-loop settings demonstrate that BridgeAD achieves state-of-the-art performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
